code: def test_classification_quality_improvement():
      # input data initialization
      train_data_path = fedot_project_root().joinpath(
          "cases/data/scoring/scoring_train.csv"
      )
      test_data_path = fedot_project_root().joinpath(
          "cases/data/scoring/scoring_test.csv"
      )
  
      seed = 50
      problem = "classification"
      with_tuning = False
  
      common_params = dict(
          problem=problem,
          n_jobs=1,
          use_pipelines_cache=False,
          use_preprocessing_cache=False,
          with_tuning=with_tuning,
          logging_level=logging.DEBUG,
          seed=seed,
      )
  
      expected_baseline_quality = 0.750
      baseline_model = Fedot(**common_params)
      baseline_model.fit(
          features=train_data_path,
          target="target",
          predefined_model="bernb",
      )
      baseline_model.predict_proba(features=test_data_path)
      baseline_metrics = baseline_model.get_metrics()
  
      # Define parameters for composing
      auto_model = Fedot(
          timeout=2,
          num_of_generations=20,
          preset="best_quality",
          **common_params,
      )
      auto_model.fit(features=train_data_path, target="target")
      auto_model.predict_proba(features=test_data_path)
      auto_metrics = auto_model.get_metrics()
  
      assert (
          auto_metrics["roc_auc_pen"]
          > baseline_metrics["roc_auc_pen"]
          >= expected_baseline_quality
      )
  
file_path: FEDOT/test/integration/quality/test_quality_improvement.py
imports: import logging
  import warnings
  from typing import Sequence
  import numpy
  from fedot import Fedot
  from fedot.core.utils import fedot_project_root
label_desc: Use image node_id 1 for calling a global function with example usage: test_classification_quality_improvement() without return types